Enhancing the Accuracy and Robustness of a Compressive Sensing Based Device-Free Localization by Exploiting Channel...

Area of Science:

• Wireless communication
• Signal processing
• Localization techniques

Background:

• Device-free localization (DFL) estimates positions using signal shadowing.
• Existing compressive sensing (CS)-based DFL methods rely on received signal strength (RSS), which is sensitive to environmental changes.
• Environmental dynamics cause deviations between runtime RSS and fixed dictionaries, degrading DFL performance.

Purpose of the Study:

• Introduce ComDec, a novel CS-based DFL method utilizing channel state information (CSI).
• Enhance localization accuracy and robustness in dynamic environments.
• Exploit channel diversity of CSI measurements for improved DFL.

Main Methods:

• Formulate the DFL problem as a joint sparse recovery problem.
• Recover multiple sparse vectors with common support using CSI.
• Develop a joint sparse recovery algorithm within the variational Bayesian inference framework.
• Parameterize dictionaries using the saddle surface model and model parameters as tunable for environmental adaptation.

Main Results:

• ComDec demonstrates superior performance compared to existing state-of-the-art CS-based DFL methods.
• The use of CSI enhances localization accuracy and robustness.
• The proposed variational Bayesian algorithm effectively solves the joint sparse recovery problem.

Conclusions:

• ComDec offers a robust and accurate solution for device-free localization.
• Leveraging CSI and advanced sparse recovery techniques addresses the limitations of RSS-based methods.
• The method's adaptability to environmental changes makes it suitable for real-world applications.